Cargando…
An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines
(1) Background: This retrospective analysis utilizing electronic medical record (EMR) data from a tertiary integrated health system sought to identify patients and prescribers who would benefit from pharmacogenomic (PGx) testing based on Clinical Pharmacogenetics Implementation Consortium (CPIC) gui...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661282/ https://www.ncbi.nlm.nih.gov/pubmed/37987388 http://dx.doi.org/10.3390/pharmacy11060178 |
_version_ | 1785148462579318784 |
---|---|
author | MacKinnon, George E. Mills, Megan Stoddard, Alexander Urrutia, Raul A. Broeckel, Ulrich |
author_facet | MacKinnon, George E. Mills, Megan Stoddard, Alexander Urrutia, Raul A. Broeckel, Ulrich |
author_sort | MacKinnon, George E. |
collection | PubMed |
description | (1) Background: This retrospective analysis utilizing electronic medical record (EMR) data from a tertiary integrated health system sought to identify patients and prescribers who would benefit from pharmacogenomic (PGx) testing based on Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. (2) Methods: EMR data from a clinical research data warehouse were analyzed from 845,518 patients that had an encounter between 2015 and 2019 at an academic medical center. Data were collected for 42 commercially available drugs with 52 evidence-based PGx guidelines from CPIC. Provider data were obtained through the EMR linked by specialty via national provider identification (NPI) number. (3) Results: A total of 845,518 patients had an encounter in the extraction period with 590,526 medication orders processed. A total of 335,849 (56.9%) patients had medication orders represented by CPIC drugs prescribed by 2803 providers, representing 239 distinct medications. (4) Conclusions: The results from this study show that over half of patients were prescribed a CPIC actionable medication from a variety of prescriber specialties. Understanding the magnitude of patients that may benefit from PGx testing, will enable the development of preemptive testing processes, physician support strategies, and pharmacist workflows to optimize outcomes should a PGx service be implemented. |
format | Online Article Text |
id | pubmed-10661282 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-106612822023-11-17 An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines MacKinnon, George E. Mills, Megan Stoddard, Alexander Urrutia, Raul A. Broeckel, Ulrich Pharmacy (Basel) Brief Report (1) Background: This retrospective analysis utilizing electronic medical record (EMR) data from a tertiary integrated health system sought to identify patients and prescribers who would benefit from pharmacogenomic (PGx) testing based on Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. (2) Methods: EMR data from a clinical research data warehouse were analyzed from 845,518 patients that had an encounter between 2015 and 2019 at an academic medical center. Data were collected for 42 commercially available drugs with 52 evidence-based PGx guidelines from CPIC. Provider data were obtained through the EMR linked by specialty via national provider identification (NPI) number. (3) Results: A total of 845,518 patients had an encounter in the extraction period with 590,526 medication orders processed. A total of 335,849 (56.9%) patients had medication orders represented by CPIC drugs prescribed by 2803 providers, representing 239 distinct medications. (4) Conclusions: The results from this study show that over half of patients were prescribed a CPIC actionable medication from a variety of prescriber specialties. Understanding the magnitude of patients that may benefit from PGx testing, will enable the development of preemptive testing processes, physician support strategies, and pharmacist workflows to optimize outcomes should a PGx service be implemented. MDPI 2023-11-17 /pmc/articles/PMC10661282/ /pubmed/37987388 http://dx.doi.org/10.3390/pharmacy11060178 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Brief Report MacKinnon, George E. Mills, Megan Stoddard, Alexander Urrutia, Raul A. Broeckel, Ulrich An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines |
title | An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines |
title_full | An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines |
title_fullStr | An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines |
title_full_unstemmed | An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines |
title_short | An EMR-Based Approach to Determine Frequency, Prescribing Pattern, and Characteristics of Patients Receiving Drugs with Pharmacogenomic Guidelines |
title_sort | emr-based approach to determine frequency, prescribing pattern, and characteristics of patients receiving drugs with pharmacogenomic guidelines |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10661282/ https://www.ncbi.nlm.nih.gov/pubmed/37987388 http://dx.doi.org/10.3390/pharmacy11060178 |
work_keys_str_mv | AT mackinnongeorgee anemrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT millsmegan anemrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT stoddardalexander anemrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT urrutiaraula anemrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT broeckelulrich anemrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT mackinnongeorgee emrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT millsmegan emrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT stoddardalexander emrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT urrutiaraula emrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines AT broeckelulrich emrbasedapproachtodeterminefrequencyprescribingpatternandcharacteristicsofpatientsreceivingdrugswithpharmacogenomicguidelines |